EasySegmentUnsupervisedTrain

Support

Required licenses

EasySegment

Recommended images

N/A

Location

[…]C:\Users\Public\Documents\Euresys\Open eVision 24.02\Sample Programs
\[LANGUAGE] samples\Deep Learning Inspection\EasySegmentUnsupervisedTrain

Purpose

This sample program demonstrates how to:

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Train an EasySegment Unsupervised tool in console mode.

Code highlights

By default, the sample program trains a tool for the dataset Fabric in the Deep Learning Additional Resources.

NOTE:

In the code, you need to replace DEEP_LEARNING_ADDITIONAL_RESOURCES by the path of the Deep Learning Additional Resources.

Select the first available GPU for the training.

// initialize a segmenter that will be trained
EUnsupervisedSegmenter newUnsupervisedSegmenter;

// make sure that we have a GPU to use (note: GPU's can only be detected in x64 configuration)
std::vector<EDeepLearningDevice> devices = newClassifier.GetAvailableDevices();

bool foundGPU = false;
for (size_t i = 0; i < devices.size(); i++)
{
  if (devices[i].GetDeviceType() == EDeepLearningDeviceType_GPU)
  {
    newClassifier.SetActiveDevice(devices[i]);
    foundGPU = true;
    break;
  }
}

Load a dataset from a Deep Learning Studio project.

const EClassificationDataset* dataset;
EDeepLearningProject project;
project.Load(PROJECT_PATH);
dataset = &project.GetDataset();

Alternatively, load a dataset directly using a .edldataset file.

const EClassificationDataset* dataset;
EClassificationDataset datasetHolder;
datasetHolder.Load(DATASET_PATH);
dataset = &datasetHolder;

Split the dataset in a training part and a validation part.

NOTE:

Be careful that the method for splitting a dataset is different for each type of tool. EasySegment Unsupervised uses the same method as EasyClassify to create a split.

// Put 80% of images into the training dataset and 20% in the validation dataset
EClassificationDataset trainingDataset;
EClassificationDataset validationDataset;
dataset->SplitDataset(trainingDataset, validationDataset, 0.8f);

Configure and train the EasySegment Unsupervised tool and wait for the training to end.

newUnsupervisedSegmenter.SetCapacity(EUnsupervisedSegmenterCapacity_Small);
newUnsupervisedSegmenter.SetForceGrayscale(true);
newUnsupervisedSegmenter.SetGoodLabel("Good");
newUnsupervisedSegmenter.Train(trainingDataset, validationDataset, 50);
newUnsupervisedSegmenter.WaitForTrainingCompletion();

Check the validation accuracy of the tool we just trained.

float newUnsupervisedSegmenterAccuracy = newUnsupervisedSegmenter.GetValidationMetrics(newUnsupervisedSegmenter.GetBestIteration()).GetBestAccuracy();
std::cout << "New segmenter Accuracy:" << newUnsupervisedSegmenterAccuracy << std::endl;